Python Pandas:为源列的每个不同值创建一个新列(布尔输出作为列值) [英] Python Pandas: create a new column for each different value of a source column (with boolean output as column values)

问题描述

我试图根据其内容将数据框的源列分成几列，然后以以下方式用布尔值1或0填充此新生成的列:

I am trying to split a source column of a dataframe in several columns based on its content, and then fill this newly generated columns with a boolean 1 or 0 in the following way:

原始数据框:

ID   source_column
A    value 1
B    NaN
C    value 2
D    value 3
E    value 2

生成以下输出:

ID   source_column    value 1    value 2    value 3
A    value 1          1          0          0
B    NaN              0          0          0
C    value 2          0          1          0
D    value 3          0          0          1
E    value 2          0          1          0

我考虑过手动创建每个不同的列，然后为每个列创建一个函数并.apply，用1或0填充新列，但这是非常无效的.

I thought about manually create each different column, and then with a function for each column and .apply, filling the newly column with a 1 or a 0, but this is highly ineffective.

有没有一种快速有效的方法?

Is there a quick and efficient way for this?

推荐答案

您可以使用pandas函数get_dummies，并将结果添加到df中，如下所示

You can use the pandas function get_dummies, and add the result to df as shown below

In [1]: col_names = df['source_column'].dropna().unique().tolist()

In [2]: df[col_names] = pd.get_dummies(df['source_column'])

In [3]: df
Out[3]: 
  ID source_column  value 1  value 2  value 3
0  A       value 1        1        0        0
1  B          NaN         0        0        0
2  C       value 2        0        1        0
3  D       value 3        0        0        1
4  E       value 2        0        1        0

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